Overview

Dataset statistics

Number of variables19
Number of observations116985
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 MiB
Average record size in memory160.0 B

Variable types

Numeric12
Categorical7

Alerts

DAY is highly overall correlated with DAWNHigh correlation
DAY_ID is highly overall correlated with LUN and 6 other fieldsHigh correlation
SQUARE_UBICAZIONI is highly overall correlated with POWER and 1 other fieldsHigh correlation
DAWN is highly overall correlated with DAY and 3 other fieldsHigh correlation
SUNSET is highly overall correlated with DAWN and 1 other fieldsHigh correlation
DAYLENGHT is highly overall correlated with DAWNHigh correlation
minTemperature is highly overall correlated with maxTemperatureHigh correlation
maxTemperature is highly overall correlated with DAWN and 2 other fieldsHigh correlation
POWER is highly overall correlated with SQUARE_UBICAZIONI and 1 other fieldsHigh correlation
POWER_NEXT is highly overall correlated with SQUARE_UBICAZIONI and 1 other fieldsHigh correlation
LUN is highly overall correlated with DAY_IDHigh correlation
MAR is highly overall correlated with DAY_IDHigh correlation
MER is highly overall correlated with DAY_IDHigh correlation
GIO is highly overall correlated with DAY_IDHigh correlation
VEN is highly overall correlated with DAY_IDHigh correlation
SAB is highly overall correlated with DAY_IDHigh correlation
DOM is highly overall correlated with DAY_IDHigh correlation
DAY_ID has 17856 (15.3%) zerosZeros
minTemperature has 1561 (1.3%) zerosZeros

Reproduction

Analysis started2023-07-03 13:15:26.022545
Analysis finished2023-07-03 13:16:03.362307
Duration37.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

SQUAREID
Real number (ℝ)

Distinct1984
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5607.0035
Minimum155
Maximum11099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:03.880087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile1574
Q13694
median5356
Q37439
95-th percentile9762
Maximum11099
Range10944
Interquartile range (IQR)3745

Descriptive statistics

Standard deviation2463.5033
Coefficient of variation (CV)0.43936183
Kurtosis-0.72305891
Mean5607.0035
Median Absolute Deviation (MAD)1831
Skewness0.15031082
Sum6.559353 × 108
Variance6068848.7
MonotonicityNot monotonic
2023-07-03T15:16:04.036670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155 59
 
0.1%
6367 59
 
0.1%
6386 59
 
0.1%
6385 59
 
0.1%
6384 59
 
0.1%
6383 59
 
0.1%
6382 59
 
0.1%
6379 59
 
0.1%
6378 59
 
0.1%
6376 59
 
0.1%
Other values (1974) 116395
99.5%
ValueCountFrequency (%)
155 59
0.1%
272 59
0.1%
273 59
0.1%
276 59
0.1%
277 59
0.1%
389 59
0.1%
390 59
0.1%
393 59
0.1%
506 59
0.1%
507 59
0.1%
ValueCountFrequency (%)
11099 59
0.1%
11098 59
0.1%
11097 59
0.1%
10984 59
0.1%
10983 59
0.1%
10982 59
0.1%
10981 59
0.1%
10980 59
0.1%
10868 59
0.1%
10867 59
0.1%

DAY
Real number (ℝ)

Distinct59
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.521528
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:04.252095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median31
Q346
95-th percentile58
Maximum60
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.461457
Coefficient of variation (CV)0.57210295
Kurtosis-1.2298115
Mean30.521528
Median Absolute Deviation (MAD)15
Skewness-0.0025654397
Sum3570561
Variance304.90247
MonotonicityNot monotonic
2023-07-03T15:16:04.436599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1984
 
1.7%
46 1984
 
1.7%
34 1984
 
1.7%
35 1984
 
1.7%
36 1984
 
1.7%
37 1984
 
1.7%
38 1984
 
1.7%
39 1984
 
1.7%
40 1984
 
1.7%
41 1984
 
1.7%
Other values (49) 97145
83.0%
ValueCountFrequency (%)
1 1984
1.7%
2 1984
1.7%
3 1984
1.7%
4 1984
1.7%
5 1984
1.7%
6 1984
1.7%
7 1984
1.7%
8 1984
1.7%
9 1913
1.6%
10 1984
1.7%
ValueCountFrequency (%)
60 1984
1.7%
59 1984
1.7%
58 1984
1.7%
57 1984
1.7%
56 1984
1.7%
55 1984
1.7%
54 1984
1.7%
53 1984
1.7%
52 1984
1.7%
51 1984
1.7%

DAY_ID
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0157456
Minimum0
Maximum6
Zeros17856
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:04.602158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0294096
Coefficient of variation (CV)0.67293793
Kurtosis-1.2694981
Mean3.0157456
Median Absolute Deviation (MAD)2
Skewness-0.021828785
Sum352797
Variance4.1185034
MonotonicityNot monotonic
2023-07-03T15:16:04.719843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 17856
15.3%
6 17856
15.3%
0 17856
15.3%
1 15872
13.6%
2 15872
13.6%
3 15872
13.6%
5 15801
13.5%
ValueCountFrequency (%)
0 17856
15.3%
1 15872
13.6%
2 15872
13.6%
3 15872
13.6%
4 17856
15.3%
5 15801
13.5%
6 17856
15.3%
ValueCountFrequency (%)
6 17856
15.3%
5 15801
13.5%
4 17856
15.3%
3 15872
13.6%
2 15872
13.6%
1 15872
13.6%
0 17856
15.3%

LUN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
99129 
1
17856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Length

2023-07-03T15:16:04.870440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:05.089855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

MAR
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
101113 
1
15872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Length

2023-07-03T15:16:05.223496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:05.372102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

MER
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
101113 
1
15872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Length

2023-07-03T15:16:05.508205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:05.646833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

GIO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
101113 
1
15872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Length

2023-07-03T15:16:05.767509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:05.895690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101113
86.4%
1 15872
 
13.6%

VEN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
99129 
1
17856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Length

2023-07-03T15:16:06.002406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:06.129106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

SAB
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
101184 
1
15801 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101184
86.5%
1 15801
 
13.5%

Length

2023-07-03T15:16:06.254730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:06.411312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101184
86.5%
1 15801
 
13.5%

Most occurring characters

ValueCountFrequency (%)
0 101184
86.5%
1 15801
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101184
86.5%
1 15801
 
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101184
86.5%
1 15801
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101184
86.5%
1 15801
 
13.5%

DOM
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
99129 
1
17856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters116985
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Length

2023-07-03T15:16:06.515074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-03T15:16:06.632758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116985
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 116985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 116985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 99129
84.7%
1 17856
 
15.3%

SQUARE_UBICAZIONI
Real number (ℝ)

Distinct343
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.744189
Minimum1
Maximum1288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:06.751441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median21
Q383
95-th percentile302
Maximum1288
Range1287
Interquartile range (IQR)78

Descriptive statistics

Standard deviation118.88384
Coefficient of variation (CV)1.7045698
Kurtosis17.743156
Mean69.744189
Median Absolute Deviation (MAD)19
Skewness3.4642563
Sum8159024
Variance14133.368
MonotonicityNot monotonic
2023-07-03T15:16:06.923939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10317
 
8.8%
2 6781
 
5.8%
3 5839
 
5.0%
4 5013
 
4.3%
5 3479
 
3.0%
6 3067
 
2.6%
7 2592
 
2.2%
8 2478
 
2.1%
9 2477
 
2.1%
10 1888
 
1.6%
Other values (333) 73054
62.4%
ValueCountFrequency (%)
1 10317
8.8%
2 6781
5.8%
3 5839
5.0%
4 5013
4.3%
5 3479
 
3.0%
6 3067
 
2.6%
7 2592
 
2.2%
8 2478
 
2.1%
9 2477
 
2.1%
10 1888
 
1.6%
ValueCountFrequency (%)
1288 59
0.1%
1198 59
0.1%
995 59
0.1%
767 59
0.1%
766 59
0.1%
730 59
0.1%
706 59
0.1%
693 59
0.1%
691 59
0.1%
685 118
0.1%

NR_LINES
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2792922
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:07.069591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.74588665
Coefficient of variation (CV)0.58304635
Kurtosis29.096694
Mean1.2792922
Median Absolute Deviation (MAD)0
Skewness4.5216147
Sum149658
Variance0.5563469
MonotonicityNot monotonic
2023-07-03T15:16:07.175269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 94814
81.0%
2 16625
 
14.2%
3 3363
 
2.9%
4 826
 
0.7%
5 649
 
0.6%
7 354
 
0.3%
6 236
 
0.2%
8 59
 
0.1%
10 59
 
0.1%
ValueCountFrequency (%)
1 94814
81.0%
2 16625
 
14.2%
3 3363
 
2.9%
4 826
 
0.7%
5 649
 
0.6%
6 236
 
0.2%
7 354
 
0.3%
8 59
 
0.1%
10 59
 
0.1%
ValueCountFrequency (%)
10 59
 
0.1%
8 59
 
0.1%
7 354
 
0.3%
6 236
 
0.2%
5 649
 
0.6%
4 826
 
0.7%
3 3363
 
2.9%
2 16625
 
14.2%
1 94814
81.0%

DAWN
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean470.00243
Minimum457
Maximum478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:07.295947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum457
5-th percentile459
Q1465
median471
Q3476
95-th percentile478
Maximum478
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.2284679
Coefficient of variation (CV)0.013251991
Kurtosis-0.99754442
Mean470.00243
Median Absolute Deviation (MAD)5
Skewness-0.497958
Sum54983234
Variance38.793812
MonotonicityNot monotonic
2023-07-03T15:16:07.416625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
477 15872
 
13.6%
476 7936
 
6.8%
475 7936
 
6.8%
474 7936
 
6.8%
473 7936
 
6.8%
470 7936
 
6.8%
478 5952
 
5.1%
469 3968
 
3.4%
472 3968
 
3.4%
471 3968
 
3.4%
Other values (11) 43577
37.3%
ValueCountFrequency (%)
457 3968
3.4%
459 3968
3.4%
460 3968
3.4%
461 3968
3.4%
462 3968
3.4%
463 3968
3.4%
464 3968
3.4%
465 3968
3.4%
466 3897
3.3%
467 3968
3.4%
ValueCountFrequency (%)
478 5952
 
5.1%
477 15872
13.6%
476 7936
6.8%
475 7936
6.8%
474 7936
6.8%
473 7936
6.8%
472 3968
 
3.4%
471 3968
 
3.4%
470 7936
6.8%
469 3968
 
3.4%

SUNSET
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990.95034
Minimum989
Maximum998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:07.533350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum989
5-th percentile989
Q1989
median990
Q3992
95-th percentile997
Maximum998
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4802086
Coefficient of variation (CV)0.0025028587
Kurtosis0.47600632
Mean990.95034
Median Absolute Deviation (MAD)1
Skewness1.24428
Sum1.1592632 × 108
Variance6.1514348
MonotonicityNot monotonic
2023-07-03T15:16:07.636075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
989 51513
44.0%
991 15872
 
13.6%
990 15872
 
13.6%
992 7936
 
6.8%
994 7936
 
6.8%
993 3968
 
3.4%
995 3968
 
3.4%
996 3968
 
3.4%
997 3968
 
3.4%
998 1984
 
1.7%
ValueCountFrequency (%)
989 51513
44.0%
990 15872
 
13.6%
991 15872
 
13.6%
992 7936
 
6.8%
993 3968
 
3.4%
994 7936
 
6.8%
995 3968
 
3.4%
996 3968
 
3.4%
997 3968
 
3.4%
998 1984
 
1.7%
ValueCountFrequency (%)
998 1984
 
1.7%
997 3968
 
3.4%
996 3968
 
3.4%
995 3968
 
3.4%
994 7936
 
6.8%
993 3968
 
3.4%
992 7936
 
6.8%
991 15872
 
13.6%
990 15872
 
13.6%
989 51513
44.0%

DAYLENGHT
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean520.45669
Minimum516
Maximum533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2023-07-03T15:16:07.744784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum516
5-th percentile516
Q1517
median518
Q3523
95-th percentile531
Maximum533
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.990606
Coefficient of variation (CV)0.0095888977
Kurtosis0.019635308
Mean520.45669
Median Absolute Deviation (MAD)2
Skewness1.1135496
Sum60885626
Variance24.906148
MonotonicityNot monotonic
2023-07-03T15:16:07.849505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
516 27776
23.7%
517 19840
17.0%
518 11904
10.2%
519 9920
 
8.5%
520 7936
 
6.8%
533 3968
 
3.4%
531 3968
 
3.4%
530 3968
 
3.4%
528 3968
 
3.4%
527 3968
 
3.4%
Other values (5) 19769
16.9%
ValueCountFrequency (%)
516 27776
23.7%
517 19840
17.0%
518 11904
10.2%
519 9920
 
8.5%
520 7936
 
6.8%
521 3968
 
3.4%
522 3897
 
3.3%
523 3968
 
3.4%
524 3968
 
3.4%
526 3968
 
3.4%
ValueCountFrequency (%)
533 3968
3.4%
531 3968
3.4%
530 3968
3.4%
528 3968
3.4%
527 3968
3.4%
526 3968
3.4%
524 3968
3.4%
523 3968
3.4%
522 3897
3.3%
521 3968
3.4%

minTemperature
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct221
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2403924
Minimum-15.4
Maximum12.7
Zeros1561
Zeros (%)1.3%
Negative46284
Negative (%)39.6%
Memory size1.8 MiB
2023-07-03T15:16:07.991126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-15.4
5-th percentile-5.1
Q1-1.8
median0.9
Q34.2
95-th percentile8.4
Maximum12.7
Range28.1
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2558363
Coefficient of variation (CV)3.4310404
Kurtosis-0.29395656
Mean1.2403924
Median Absolute Deviation (MAD)3
Skewness0.13287084
Sum145107.3
Variance18.112142
MonotonicityNot monotonic
2023-07-03T15:16:08.142720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 2196
 
1.9%
0.4 1642
 
1.4%
0 1561
 
1.3%
5.3 1419
 
1.2%
-1.7 1398
 
1.2%
-0.6 1358
 
1.2%
-0.5 1306
 
1.1%
-2.4 1284
 
1.1%
3.8 1246
 
1.1%
2.5 1225
 
1.0%
Other values (211) 102350
87.5%
ValueCountFrequency (%)
-15.4 25
 
< 0.1%
-15 25
 
< 0.1%
-14.5 25
 
< 0.1%
-12.4 43
< 0.1%
-11.9 44
< 0.1%
-11.8 43
< 0.1%
-11.4 43
< 0.1%
-10.7 16
 
< 0.1%
-10.5 44
< 0.1%
-9.6 68
0.1%
ValueCountFrequency (%)
12.7 42
 
< 0.1%
12.6 79
 
0.1%
12.2 212
0.2%
11.8 224
0.2%
11.7 130
0.1%
11.6 49
 
< 0.1%
11.5 168
0.1%
11.4 138
0.1%
11.2 49
 
< 0.1%
11.1 91
0.1%

maxTemperature
Real number (ℝ)

Distinct213
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5437595
Minimum-6.7
Maximum19.3
Zeros146
Zeros (%)0.1%
Negative1588
Negative (%)1.4%
Memory size1.8 MiB
2023-07-03T15:16:08.304289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-6.7
5-th percentile2
Q15.9
median8.4
Q311.1
95-th percentile15.5
Maximum19.3
Range26
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation3.9929373
Coefficient of variation (CV)0.46735132
Kurtosis-0.06387022
Mean8.5437595
Median Absolute Deviation (MAD)2.6
Skewness0.044286435
Sum999491.7
Variance15.943548
MonotonicityNot monotonic
2023-07-03T15:16:08.467850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.8 1932
 
1.7%
8.1 1819
 
1.6%
8.3 1593
 
1.4%
7.5 1575
 
1.3%
7.2 1519
 
1.3%
7.4 1502
 
1.3%
6.9 1500
 
1.3%
9.4 1495
 
1.3%
9.5 1440
 
1.2%
8.4 1386
 
1.2%
Other values (203) 101224
86.5%
ValueCountFrequency (%)
-6.7 25
 
< 0.1%
-5 43
< 0.1%
-4.5 16
 
< 0.1%
-4.2 25
 
< 0.1%
-4.1 25
 
< 0.1%
-3.9 25
 
< 0.1%
-3.7 43
< 0.1%
-3.1 25
 
< 0.1%
-3 71
0.1%
-2.7 25
 
< 0.1%
ValueCountFrequency (%)
19.3 52
 
< 0.1%
19.2 145
0.1%
19.1 79
 
0.1%
18.9 276
0.2%
18.8 134
0.1%
18.7 70
 
0.1%
18.5 71
 
0.1%
18.4 89
 
0.1%
18.1 65
 
0.1%
18 150
0.1%

POWER
Real number (ℝ)

Distinct96953
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6527645
Minimum-54.133309
Maximum240.74708
Zeros289
Zeros (%)0.2%
Negative4040
Negative (%)3.5%
Memory size1.8 MiB
2023-07-03T15:16:08.629378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-54.133309
5-th percentile0.0091371934
Q10.10689682
median0.48430504
Q32.2526717
95-th percentile17.375234
Maximum240.74708
Range294.88039
Interquartile range (IQR)2.1457749

Descriptive statistics

Standard deviation11.512313
Coefficient of variation (CV)3.1516713
Kurtosis82.214674
Mean3.6527645
Median Absolute Deviation (MAD)0.45403
Skewness7.5146575
Sum427318.66
Variance132.53335
MonotonicityNot monotonic
2023-07-03T15:16:08.776983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 289
 
0.2%
0.01718592839 20
 
< 0.1%
0.09165828476 16
 
< 0.1%
0.005728642797 16
 
< 0.1%
0.07447235637 12
 
< 0.1%
0.1145728559 12
 
< 0.1%
0.01145728559 12
 
< 0.1%
0.02790126128 8
 
< 0.1%
0.02672989848 8
 
< 0.1%
0.02803377885 8
 
< 0.1%
Other values (96943) 116584
99.7%
ValueCountFrequency (%)
-54.1333095 1
< 0.1%
-45.72629119 1
< 0.1%
-45.56204648 1
< 0.1%
-42.91868572 1
< 0.1%
-41.71932919 1
< 0.1%
-40.04808376 1
< 0.1%
-38.40058602 1
< 0.1%
-38.15609155 1
< 0.1%
-37.75673651 1
< 0.1%
-36.84545711 1
< 0.1%
ValueCountFrequency (%)
240.7470789 1
< 0.1%
232.2171792 1
< 0.1%
228.1657362 1
< 0.1%
228.1392056 1
< 0.1%
228.1014264 1
< 0.1%
227.2958441 1
< 0.1%
226.5436171 1
< 0.1%
223.7210199 1
< 0.1%
223.4948605 1
< 0.1%
223.4836613 1
< 0.1%

POWER_NEXT
Real number (ℝ)

Distinct96995
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6887768
Minimum-54.133309
Maximum240.74708
Zeros283
Zeros (%)0.2%
Negative3945
Negative (%)3.4%
Memory size1.8 MiB
2023-07-03T15:16:08.938591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-54.133309
5-th percentile0.0095277704
Q10.10828662
median0.49096337
Q32.2781787
95-th percentile17.540332
Maximum240.74708
Range294.88039
Interquartile range (IQR)2.1698921

Descriptive statistics

Standard deviation11.571251
Coefficient of variation (CV)3.1368802
Kurtosis81.371847
Mean3.6887768
Median Absolute Deviation (MAD)0.46006751
Skewness7.4805199
Sum431531.56
Variance133.89385
MonotonicityNot monotonic
2023-07-03T15:16:09.088191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 283
 
0.2%
0.01718592839 15
 
< 0.1%
0.005728642797 12
 
< 0.1%
0.09165828476 12
 
< 0.1%
0.1145728559 9
 
< 0.1%
0.07447235637 9
 
< 0.1%
0.01145728559 9
 
< 0.1%
0.02464890798 8
 
< 0.1%
0.03282605361 8
 
< 0.1%
0.02632623066 8
 
< 0.1%
Other values (96985) 116612
99.7%
ValueCountFrequency (%)
-54.1333095 1
< 0.1%
-45.56204648 1
< 0.1%
-41.71932919 1
< 0.1%
-40.04808376 1
< 0.1%
-38.40058602 1
< 0.1%
-38.15609155 1
< 0.1%
-37.75673651 1
< 0.1%
-36.84545711 1
< 0.1%
-34.10222283 1
< 0.1%
-32.87273614 1
< 0.1%
ValueCountFrequency (%)
240.7470789 1
< 0.1%
232.2171792 1
< 0.1%
228.1657362 1
< 0.1%
228.1392056 1
< 0.1%
228.1014264 1
< 0.1%
227.2958441 1
< 0.1%
226.5436171 1
< 0.1%
223.7210199 1
< 0.1%
223.4948605 1
< 0.1%
223.4836613 1
< 0.1%

Interactions

2023-07-03T15:16:00.601063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:38.544724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.500559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:42.559987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:44.513759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:46.485486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:48.581881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:50.749917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:52.613184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:54.595841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:56.762086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:58.678917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:00.756645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:38.728236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.658116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:42.718604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:44.673376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:46.637120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:48.772370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:50.904504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:52.786679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:54.761399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:56.919623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:58.844475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:00.914226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:38.903805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.824667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:42.877138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:44.844875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:46.804675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:48.952887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.061047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:52.952278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:55.126420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:57.078200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.001056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:01.075793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:39.062408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.988231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:43.044728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.019449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:46.958263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:49.136394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.218663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:53.116797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:55.304943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:57.234821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.161649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:01.239422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:39.222910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:41.154747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:43.215234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.192942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:47.115801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:49.319904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.376201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:53.287341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:55.486457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:57.403332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.320245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:01.390989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:39.375545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:41.309332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:43.376802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.354552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:47.265439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:49.510394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.523878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:53.449972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:55.636058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:57.553968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.474859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:01.536602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:39.524105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:41.468946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:43.529394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.506142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:47.409017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:49.684927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.667423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:53.609477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:55.792680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:57.707518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.631372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:01.686160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:39.675700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:41.627481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:43.682980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.661754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:47.562603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:49.874423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.821328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:53.768093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:55.950255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:57.862145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.793936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:01.859697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:39.858252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:41.803009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:43.852527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.834296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:47.858811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:50.055952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:51.982898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:53.936602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:56.119763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:58.025666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:59.954545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:02.015281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.015831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:41.958593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:44.016090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:45.998828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:48.011404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:50.218518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:52.142401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:54.108143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:56.284323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:58.185281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:00.122098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:02.178843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.174407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:42.117169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:44.179694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:46.160426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:48.197909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:50.411784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:52.295991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:54.268714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:56.437914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:58.351792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:00.275714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:02.338454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:40.331944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:42.387448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:44.349201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:46.322921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:48.395377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:50.590306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:52.449620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:54.431279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:56.600519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:15:58.519344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-03T15:16:00.436542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-07-03T15:16:09.256701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
SQUAREIDDAYDAY_IDSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERPOWER_NEXTLUNMARMERGIOVENSABDOM
SQUAREID1.0000.001-0.000-0.064-0.0750.0000.000-0.000-0.290-0.298-0.046-0.0440.0000.0000.0000.0000.0000.0000.000
DAY0.0011.000-0.015-0.000-0.0000.5230.405-0.394-0.461-0.4940.0550.0520.1450.2020.2010.2020.1450.1890.129
DAY_ID-0.000-0.0151.0000.0000.000-0.0250.0320.0350.037-0.033-0.024-0.0111.0001.0001.0001.0001.0001.0001.000
SQUARE_UBICAZIONI-0.064-0.0000.0001.0000.361-0.000-0.0000.0000.0460.0490.8010.8020.0000.0000.0000.0000.0000.0000.000
NR_LINES-0.075-0.0000.0000.3611.000-0.000-0.0000.0000.0490.0620.4040.4040.0000.0000.0000.0000.0000.0000.000
DAWN0.0000.523-0.025-0.000-0.0001.0000.756-0.797-0.163-0.5450.0310.0280.3430.2620.2370.2940.3260.2360.215
SUNSET0.0000.4050.032-0.000-0.0000.7561.000-0.487-0.145-0.5110.0200.0240.4040.3030.2770.3360.3610.2770.333
DAYLENGHT-0.000-0.3940.0350.0000.000-0.797-0.4871.0000.0200.458-0.027-0.0230.3260.3800.3480.2870.3740.3040.264
minTemperature-0.290-0.4610.0370.0460.049-0.163-0.1450.0201.0000.587-0.003-0.0030.0600.1100.0760.1180.1360.1230.076
maxTemperature-0.298-0.494-0.0330.0490.062-0.545-0.5110.4580.5871.000-0.016-0.0070.1420.1380.1100.1930.0740.0830.132
POWER-0.0460.055-0.0240.8010.4040.0310.020-0.027-0.003-0.0161.0000.9740.0100.0070.0030.0110.0020.0170.028
POWER_NEXT-0.0440.052-0.0110.8020.4040.0280.024-0.023-0.003-0.0070.9741.0000.0050.0000.0090.0090.0180.0280.008
LUN0.0000.1451.0000.0000.0000.3430.4040.3260.0600.1420.0100.0051.0000.1680.1680.1680.1800.1680.180
MAR0.0000.2021.0000.0000.0000.2620.3030.3800.1100.1380.0070.0000.1681.0000.1570.1570.1680.1570.168
MER0.0000.2011.0000.0000.0000.2370.2770.3480.0760.1100.0030.0090.1680.1571.0000.1570.1680.1570.168
GIO0.0000.2021.0000.0000.0000.2940.3360.2870.1180.1930.0110.0090.1680.1570.1571.0000.1680.1570.168
VEN0.0000.1451.0000.0000.0000.3260.3610.3740.1360.0740.0020.0180.1800.1680.1680.1681.0000.1680.180
SAB0.0000.1891.0000.0000.0000.2360.2770.3040.1230.0830.0170.0280.1680.1570.1570.1570.1681.0000.168
DOM0.0000.1291.0000.0000.0000.2150.3330.2640.0760.1320.0280.0080.1800.1680.1680.1680.1800.1681.000

Missing values

2023-07-03T15:16:02.562815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-03T15:16:02.973716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SQUAREIDDAYDAY_IDLUNMARMERGIOVENSABDOMSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERPOWER_NEXT
0155140000100414579915339.218.90.0735310.129407
11552500000104145999053111.815.90.1294070.103237
21553600000014146099053011.019.20.1032370.178362
3155401000000414619895288.611.50.1783620.109082
4155510100000414629895277.416.60.1090820.122315
5155620010000414639895266.517.40.1223150.124060
6155730001000414649895247.617.00.1240600.140293
71558400001004146598952310.513.50.1402930.186533
8155950000010414669895228.012.60.1865330.104067
91551060000001414679895215.612.70.1040670.132174
SQUAREIDDAYDAY_IDLUNMARMERGIOVENSABDOMSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERPOWER_NEXT
12094211099515000001031475991516-2.13.90.3595530.389883
12094311099526000000131475992516-0.53.40.3898830.409328
12094411099530100000031476992516-1.43.10.4093280.416777
12094511099541010000031476993516-0.54.90.4167770.420241
120946110995520010000314779945170.23.60.4202410.330085
120947110995630001000314779945170.10.90.3300850.300136
12094811099574000010031477995517-3.42.00.3001360.319235
12094911099585000001031477996518-4.12.80.3192350.332945
12095011099596000000131478997519-2.62.20.3329450.326883
12095111099600100000031478998519-7.7-1.40.3268830.334984